Interpretive Summary: Developing a detection technology to find the presence of fecal contamination on the surface of poultry carcasses is critical to food safety. A statistical detection technique was developed to discriminate between a contaminated bird and an uncontaminated bird. Multispectral image data of fecal materials and uncontaminated normal carcasses were statistically modeled with two distribution estimators. A projection was used to reduce the variation of multispectral data and to model the statistical characteristics. Thresholding of data on two perpendicular projection axes was studied to develop a fecal material detection algorithm. A linear mixture model of statistical distribution estimates was utilized to designed thresholds at both axes. The detection performance of the developed multi-thresholding technique is similar to the single threshold technique while more reducing false positives. This technique can be incorporated into a real-time multispectral imaging system to detect visible fecal contaminants on boiler carcasses.

Technical Abstract:
Recently, hyperspectral image analysis has proved successful for a target detection problem encountered in remote sensing as well as near sensing utilizing in situ instrumentation. The conventional global bi-level thresholding for target detection, such as the clustering-based Otsu’s method, has been inadequate for the detection of biologically harmful material on foods that has a large degree of variability in size, location, color, shape, texture, and occurrence time. This paper presents multistep-like thresholding based on kernel density estimation for the real-time detection of harmful contaminants on a food product presented in multispectral images. We are particularly concerned with the detection of fecal contaminants on poultry carcasses in real-time. In the past, we identified 2 optimal wavelength bands and developed a real-time multispectral imaging system using a common aperture camera and a globally optimized thresholding method from a ratio of the optimal bands. This work extends our previous study by introducing a new decision rule to detect fecal contaminants on a single bird level. The underlying idea is to search for statistical separability along the two directions defined by the global optimal threshold vector and its orthogonal vector. Experimental results with real birds and fecal samples in different amounts are provided.